JMIR Medical Education

Generative artificial intelligence (AI) has shown rapid advancements and increasing applications in various domains, including healthcare. Previous studies have evaluated AI performance on medical license exams, primarily focusing on ChatGPT. However, the availability of new online chat-based large language models (OC-LLMs) and their potential utility in pharmacy licensing examinations remain underexplored. Considering that pharmacists require a broad range of expertise in physics, chemistry, biology, and pharmacology, verifying the knowledge base and problem-solving abilities of these new models in Japanese pharmacy examinations is necessary.

Applying functional anatomy to clinical examination techniques in shoulder examination is challenging for physicians in all learning stages. Anatomy teaching has shifted towards a more function-oriented approach and welcomed e-learning to a greater extent. There is limited evidence on whether the integrated teaching of professionalism, clinical examination technique and functional anatomy via e-learning is effective.

Effective diabetes management requires behavioral change support from primary care providers. However, general practitioners (GPs) often lack training in patient-centered communication methods such as motivational interviewing (MI), especially in time-constrained settings. While brief MI offers a practical alternative, evidence on its impact among GPs and patient outcomes remains limited.

Medical education now focuses on developing skilled and dependable professionals, with particular attention to the hidden curriculum and its influence on professionalism and humanism.

Artificial intelligence (AI) is increasingly embedded in medical education, providing benefits in instructional design, content creation, and administrative efficiency. Tools like ChatGPT are reshaping training and teaching practices in digital health. However, concerns about faculty overreliance highlight risks to pedagogical autonomy, cognitive engagement, and ethics. Despite global interest, there is limited empirical research on AI dependency among medical educators, particularly in underrepresented regions like the Global South.

The increasing prevalence of dermatological diseases will pose a growing challenge to the healthcare system and, in particular, to general practitioners (GP) as the first point of contact for these patients. In many countries, primary care physicians are supported by teledermatology services.

The integration of digital health and informatics competencies into healthcare education in Canada is essential for preparing a workforce capable of leveraging healthcare technologies to enhance care delivery and patient outcomes. Despite significant advancements, the current educational landscape in digital health remains inconsistent, characterized by fragmented curricula and uneven competency attainment. Addressing these gaps requires an innovative reframing of digital health competencies guided by a robust, outcomes-oriented framework. These authors propose the Quintuple Aim as an effective framework for outlining and organizing digital health and informatics competencies, focusing simultaneously on improving patient experience, enhancing population health, reducing healthcare costs, improving healthcare provider experience, and advancing health equity. Each dimension of the Quintuple Aim provides a critical lens for identifying, prioritizing, and contextualizing core competencies. Within the "patient experience" aim, competencies prioritize patient-centered technology use, including digital literacy, privacy awareness, and the ability to empower patients through technology. "Healthcare provider experience" competencies prioritize usability, workflow integration, and strategies to mitigate technology-related burnout. Under "population health," competencies emphasize data-driven decision-making, analytics, and health informatics to support effective public health interventions. Competencies associated with "cost reduction" focus on operational efficiency, resource optimization, and economic evaluation of digital health solutions. Lastly, "health equity" competencies emphasize inclusivity, cultural safety, and the elimination of digital divides, ensuring equitable access to digital health technologies. Potential assessment strategies aligned with each competency area are highlighted, emphasizing formative and summative evaluations that include simulation-based assessments, real-world technology integration projects, and reflective practice portfolios. By applying the Quintuple Aim as a guiding structure, digital health education can achieve greater standardization, clarity, and alignment with healthcare system needs, while simultaneously allowing for tailored adaptations responsive to specific regional and institutional priorities. This paper introduces the Quintuple Aim as a guiding framework to comprehensively identify and organize core digital health and informatics competencies for health professional education.

Learning style is a biologically and developmentally imposed configuration of personal characteristics which make the same teaching method effective for some and ineffective for others. Studies support a relationship between learning style and medicine’s career choice resulting in learning style patterns observed in distinct types of residency programs, which can also be applied to general surgery, from medical school to latest stages of training. The methodologies, populations, and contexts of the few studies pertinent to the matter are very different from one another, and a scoping review on this theme will unequivocally enhance and organize what is already known.

At the beginning of their clinical clerkships (CCs), medical students face multiple challenges related to acquiring clinical and communication skills, building professional relationships, and managing psychological stress. While mentoring and structured feedback are known to provide critical support, existing systems may not offer sufficient and timely guidance owing to the faculty’s limited availability. Generative artificial intelligence, particularly large language models, offers new opportunities to support medical education by providing context-sensitive responses.

The necessity for self-regulated, lifelong learners in the rapidly evolving field of medicine underscores the importance of effective study skills. Efforts to support students with these skills have had positive outcomes but are often limited in scope and accessibility, with a tendency to target groups facing immediate challenges.

History-taking is crucial in medical training. However, current methods often lack consistent feedback and standardized evaluation, and have limited access to standardized patient (SP) resources. Artificial Intelligence (AI)-powered simulated patients offer a promising solution; however, challenges such as human-AI consistency, evaluation stability and transparency remain underexplored in multi-case clinical scenarios.

Digital tools, services and information in patient care demand new competences in outpatient care and the workforce is faced with the need to deal with digitalization.